Wastewaterrn rnis usedrn rnwaterrn rnfromrn rnanyrn rncombination ofrn rndomestic,rn rnindustrial,rn rncommercialrn rnor agricultural rnrnactivities, surface runoff or stormwater, and any sewer inflow or sewer infiltration.rn rnrnThe characteristics of rnrnwastewaterrn rnrnvaryrn rnrndepending onrn rnrnthern rnrnsource.rn rnrnTypes ofrn rnrnwastewaterrn rnrnincludern rnrndomesticrn rnrnwastewaterrn rnrnfrom rnrnhouseholds,rn rnrnmunicipal wastewater from communities (also called sewage) or industrial wastewater from rnrnindustrialrn rnrnactivities.Wastewaterrn rnrntreatmentrn rnrnis thern rnrnprocess ofrn rnrntreatingrn rnrncontaminantsrn rnrnpriorrn rnrnto releasing rnrnwastewater into the environment or reusing. Basically, there are four steps to remove contaminants in rnrnsewagern rnrnwastewaterrn rnrnwhichrn rnrnare;rn rnrnpretreatment,rn rnrnprimaryrn rnrntreatment,rn rnrnsecondaryrn rnrntreatmentrn rnrnandrn rnrntertiary rnrntreatment.The activated sludge process is a biological process and an essential secondary treatment in wastewater treatment , where bacteria plays a role of degrading organic substances based on the the crucial process control parameter, dissolved oxygen (DO) concentration. The DO concentration in the aeration tank(s) is maintained at the desired level by manipulation of airflow rate, applying a Neural network basedrnadaptive Proportional-Integral-Derivative (PID) controller.rnrnrnIn this thesis work, an Adaptive Neural Network Radial Basis Function PID (ANNRBFPID) control strategy is implemented to control a DO concentration in aerated bioreactors which update the set point of DO adaptively and withstand uncertain disturbances. Two models are selected to represent an activated sludgernprocess. The first one is the simplified model with only four state variables. The second model is the rnrnActivatedrn rnrnSludgern rnrnModelrn rnrnno.1(ASM1)rn rnrnthern rnrnmorern rnrnrealisticrn rnrnandrn rnrnacceptedrn rnrnmodelrn rnrnwithrn rnrn13 statern rnrnvariables. rnrnMatlab/Simulink and SIMBA# software used for simulating the designed mathematical model and control of the activated sludge process for the simplified model and ASM1 respectively. The powerful learning and adaptive ability of the RBF neural network make the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters onlinerncan be made by ANNRBFPID algorithm to improve the performance of the controller.rnrnrnThe Matlab/Simulink simulation result show that the DO can be maintained at 2mg/L or any desired setpointrnrnwith the presence of uncertain disturbances and continuously variable influents with ANNRBFPID control rnrnalgorithm and the simulation resultrn rnrnshows that ANNRBFPID achieve better control performance than rnconventional PID. On the other hand, SIMBA# simulation results show that the international standard limit for Ntot (Total Nitrogen), CODtot (Total Chemical Oxygen Demand), SNH (NH4(+) and NH3 nitrogen), rnrnTSS(Total Suspended Solids) is given byrn rnrn< 18g, < 100g, < 4g, < 30g respectively and the simulation result rnrnobtained is 11.04 g N/mÂ³, 23.82 g COD/mÂ³,rn rnrn0.5421 g N/mÂ³, 5.061 g/mÂ³ respectively.